Benjamin Sherman
Machine Learning Engineer at Weights & Biases
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolBenjamin Sherman is a Machine Learning Engineer in San Francisco with a decade of software experience and four years focused on ML and data engineering, currently building production ML infrastructure at Weights & Biases. He previously developed automated, scalable training and deployment pipelines for Ads AI at Samsung, including fully automated hyperparameter tuning, distributed training with Horovod, and Terraform-backed CI/CD on AWS. Comfortable across the stack, he’s contributed backend and DevOps enhancements to the widely used wandb platform—improving Git integration, launch infrastructure, and base-image support for reproducible jobs. His academic work produced order-of-magnitude speedups in protein data analysis and novel graph-matching algorithms, reflecting a knack for turning research into high-performance tools. Colleagues can expect a pragmatic engineer who blends rigorous CS foundations with hands-on experience shipping reliable ML systems at scale.
10 years of coding experience
3 years of employment as a software developer
University of California Santa Cruz
Github Skills (10)
kubernetes10
docker10
python10
dockers10
kubernetes-pods10
cicd9
git9
kaniko8
gcp8
aws8
Programming languages (7)
Github contributions (5)
wandb/wandb
Jul 2022 - Jan 2023
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:2 releases, 320 reviews, 63 commits in 6 months
Contributions summary:Benjamin implemented several enhancements to the Weights & Biases platform, focusing on the Git integration and launch features. They added functionality to specify a custom root directory for Git repositories using settings or environment variables, along with code to manage and install dependencies. The user also worked on the launch infrastructure, introducing environment and registry classes, supporting custom Kubernetes object deployments and addressing issues related to the base image support for a better developer experience. The user implemented a base image and environment settings so jobs can be launched from existing images.
pythoncollaborationtensorflowhyperparameter-tuningcli
bcsherma/superres
May 2022 - Jul 2022
Contributions:42 commits, 22 pushes in 2 months
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